Standard

msreg : A command for consistent estimation of linear regression models using matched data. / Hirukawa, Masayuki; Liu, Di; Prokhorov, Artem.

In: Stata Journal, Vol. 21, No. 1, 03.2021, p. 123-140.

Research output: Contribution to journal › Review article › peer-review

Harvard

Hirukawa, M, Liu, D & Prokhorov, A 2021, 'msreg: A command for consistent estimation of linear regression models using matched data', Stata Journal, vol. 21, no. 1, pp. 123-140. https://doi.org/10.1177/1536867x211000008

APA

Hirukawa, M., Liu, D., & Prokhorov, A. (2021). msreg: A command for consistent estimation of linear regression models using matched data. Stata Journal, 21(1), 123-140. https://doi.org/10.1177/1536867x211000008

Vancouver

Hirukawa M, Liu D, Prokhorov A. msreg: A command for consistent estimation of linear regression models using matched data. Stata Journal. 2021 Mar;21(1):123-140. https://doi.org/10.1177/1536867x211000008

Author

Hirukawa, Masayuki ; Liu, Di ; Prokhorov, Artem. / msreg : A command for consistent estimation of linear regression models using matched data. In: Stata Journal. 2021 ; Vol. 21, No. 1. pp. 123-140.

BibTeX

@article{a859b69ec30043baaa47459833daa0a5,
title = "msreg: A command for consistent estimation of linear regression models using matched data",
abstract = "Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We describe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four.",
keywords = "bias correction, linear regression, matching estimation, msreg, st0630",
author = "Masayuki Hirukawa and Di Liu and Artem Prokhorov",
note = "Publisher Copyright: {\textcopyright} StataCorp LLC 2021.",
year = "2021",
month = mar,
doi = "10.1177/1536867x211000008",
language = "English",
volume = "21",
pages = "123--140",
journal = "Stata Journal",
issn = "1536-867X",
publisher = "DPC Nederland",
number = "1",

}

RIS

TY - JOUR

T1 - msreg

T2 - A command for consistent estimation of linear regression models using matched data

AU - Hirukawa, Masayuki

AU - Liu, Di

AU - Prokhorov, Artem

N1 - Publisher Copyright: © StataCorp LLC 2021.

PY - 2021/3

Y1 - 2021/3

N2 - Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We describe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four.

AB - Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We describe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four.

KW - bias correction

KW - linear regression

KW - matching estimation

KW - msreg

KW - st0630

UR - http://www.scopus.com/inward/record.url?scp=85103596135&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/3b3bbe57-4dcd-38bc-af46-d5809c324f5c/

U2 - 10.1177/1536867x211000008

DO - 10.1177/1536867x211000008

M3 - Review article

AN - SCOPUS:85103596135

VL - 21

SP - 123

EP - 140

JO - Stata Journal

JF - Stata Journal

SN - 1536-867X

IS - 1

ER -

ID: 85598804